The theory of generalized linear models provides a unifying class of statistical distributions that can be used to model both discrete and continuous events. In this dissertation we present a new conjugate hierarchical ...

To save time and reduce the size and cost of clinical trials, surrogate endpoints are frequently measured instead of true endpoints. The proportion of the treatment effect explained by surrogate endpoints (PTE) is a widely ...

The process of conducting a pharmaceutical clinical trial often produces information in a way that can be used as the trial progresses. Bayesian methods offer a highly flexible means of using such information yielding ...

Correlated binary measurements can occur in a variety of practical contexts and afford interesting statistical modeling challenges. In order to model the separate probabilities for each measurement we must somehow account ...

Clinical trial endpoints are traditionally either physical or laboratory responses. However, such endpoints fail to reflect how patients feel or function in their daily activities. Missing data is inevitable in most every ...

This dissertation consists of three selected topics in statistical discriminant analysis: dimension reduction, regularization methods, and imputation methods. In Chapter 2 we first derive a new linear dimension-reduction ...

In a variety of regression applications, measurement problems are unavoidable because infallible measurement tools may be expensive or unavailable. When modeling the relationship between a response variable and covariates, ...

The efficacy, safety, and cost of pharmaceutical products are critical issues in society today. Motivated both financially and ethically by these concerns, the pharmaceutical industry has continually worked to develop ...

Sample size determination is one of the most important aspects in clinical designs. Careful selection of appropriate sample sizes can not only save economic and human resources, but also improve model performance and ...

Our objective in this work is to monitor a production process yielding output that is correlated and contaminated with autocorrelated measurement error. Often, the elimination of the causes of the autocorrelation of the ...

Adaptive designs are increasingly popular in clinical trials. This is because such designs have the potential to decrease patient exposure to treatments that are
less efficacious or unsafe. The Bayesian approach to ...

The change-point (CP) problem, wherein parameters of a model change abruptly
at an unknown covariate value, is common in many fields, such as process control,
epidemiology, and ecology. CP problems using two-segment ...

In medical diagnosis and treatment, many diseases are characterized by multiple measurable differences in clinical (e.g., physical or radiological differences) and laboratory parameters (biomarkers from "healthy levels". ...

We present interval estimation methods for comparing Poisson rate parameters from two independent populations with under-reported data for the rate difference and the rate ratio. In addition, we apply the Bayesian paradigm ...

Because of the high cost and time constraints for clinical trials, researchers often need to determine the smallest sample size that provides accurate inferences for a parameter of interest or need to adaptive design ...

This dissertation is comprised of four chapters. In the first chapter, we define
the concept of linear dimension reduction, review some popular linear dimension
reduction procedures, discuss background research that we ...

Lectio Divina is a musical exploration of the contemplative prayer and scripture‐reading practice called "Lectio Divina". The work is written for a chamber ensemble: flute, clarinet, violin, cello, piano and percussion. ...

Mismeasurment, and specifically misclassification, are inevitable in a variety of regression applications. Fallible measurement methods are often used when infallible methods are either expensive or not available. Ignoring ...

We first consider the problem of discrete censored sampling. Censored binomial data may lead to irregular likelihood functions and problems with statistical inference. We consider a Bayesian approach to inference for ...